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Bayesian Semi-parametric Design (BSD) for adaptive dose-finding with multiple strata.

Mo Li1, Rachael Liu2, Jianchang Lin2

  • 1Department of Biostatistics, Yale University , New Haven, CT, USA.

Journal of Biopharmaceutical Statistics
|March 5, 2020
PubMed
Summary
This summary is machine-generated.

New Bayesian semi-parametric designs (BSD) efficiently identify the maximum tolerated dose (MTD) in oncology trials with multiple patient strata. These adaptive methods improve MTD identification and reduce sample size requirements.

Keywords:
Adaptive designsBayesian Dose findingBayesian semi-parametric modelDirichlet processmaximum tolerated dosemultiple strata

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Area of Science:

  • Oncology
  • Biostatistics
  • Clinical Trial Design

Background:

  • Precision medicine necessitates identifying the maximum tolerated dose (MTD) within specific patient strata in oncology dose-finding studies.
  • Current methods for multi-strata dose-finding may lack efficiency or flexibility in handling diverse toxicity profiles.

Purpose of the Study:

  • To introduce two novel Bayesian semi-parametric designs (BSD) for adaptive dose-finding in multi-strata oncology studies.
  • To enable efficient and accurate identification of the MTD for each stratum while allowing for flexible toxicity profile modeling.

Main Methods:

  • Development of two BSD models utilizing non-parametric Dirichlet process priors for flexible prior distributions.
  • Models incorporate varying assumptions of strata heterogeneity, enabling adaptive information borrowing across similar strata.
  • Comparison with existing methods (fully stratified, exchangeability, and exchangeability-non-exchangeability models) via simulation studies.

Main Results:

  • The proposed BSD models demonstrated superior performance in correctly identifying the MTD across different strata compared to existing methods.
  • BSD models required a smaller sample size for MTD determination.
  • The models proved robust to different assumptions regarding strata heterogeneity.

Conclusions:

  • Bayesian semi-parametric designs offer an effective approach for multi-strata oncology dose-finding studies.
  • These methods enhance MTD identification accuracy and efficiency, with potential for extension to other endpoints.